Assessment of Spatiotemporal Variability and Trend Analysis of Reference Crop Evapotranspiration for the southern region of Peninsular India

Abstract Accurate estimation of reference evapotranspiration (ET0) is an essential requirement for water resource management and scheduling agricultural activities. Several empirical methods have been employed in estimating ET0 across diverse climate regimes over the past decades. The Python implementation for estimation of daily and monthly ET0 values of representative stations of ten agro-climatic zones of Karnataka from 1979 through 2014 using the standard FAO Penman-Monteith method was carried out. The assessment of temporal and spatial variability of monthly ET0 values across the various agro-climatic zones done by the various statistical measures revealed that the variation in spatial ET0 values was higher than temporal indicating major differentiation of ET0 values was with respect to the stations rather than years under study. The non-parametric Mann-Kendall test conducted at 1% significance level on the annual ET0 values revealed that statistically significant increasing trend was observed for all the ten stations during the study period. The trend test conducted on the climate variables like mean air temperature, wind speed, relative humidity and solar radiation signify their influence the annual ET0 values. The magnitude changes in the trends detected by the Theil Sen’s slope indicated that increasing values of mean temperature, solar radiation and decreasing values of relative humidity predominantly contributed to the annual upward trend in ET0 values for the 10 stations. A trivial impact of wind speed on annual ET0 values was observed for the stations. Kalburgi and Udupi stations exhibited positive ET0 trend with the highest and lowest annual values among ten stations.

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Monthly pan evaporation modelling using multiple linear regression and artificial neural network techniques

Water Science & Technology Water Supply ◽

10.2166/ws.2019.189 ◽

2019 ◽

Vol 20 (3) ◽

pp. 800-808

Author(s):

G. T. Patle ◽

M. Chettri ◽

D. Jhajharia

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Wind Speed ◽

Relative Humidity ◽

Linear Regression ◽

Multiple Linear Regression ◽

Accurate Estimation ◽

Pan Evaporation ◽

Ann Models ◽

Artificial Neural

Abstract Accurate estimation of evaporation from agricultural fields and water bodies is needed for the efficient utilisation and management of water resources at the watershed and regional scale. In this study, multiple linear regression (MLR) and artificial neural network (ANN) techniques are used for the estimation of monthly pan evaporation. The modelling approach includes the various combination of six measured climate parameters consisting of maximum and minimum air temperature, maximum and minimum relative humidity, sunshine hours and wind speed of two stations, namely Gangtok in Sikkim and Imphal in the Manipur states of the northeast hill region of India. Average monthly evaporation varies from 0.62 to 2.68 mm/day for Gangtok, whereas it varies from 1.4 to 4.3 mm/day for Imphal during January and June, respectively. Performance of the developed MLR and ANN models was compared using statistical indices such as coefficient of determination (R2), root mean square error (RMSE) and mean absolute error (MAE) with measured pan evaporation values. Correlation analysis revealed that temperature, wind speed and sunshine hour had positive correlation, whereas relative humidity had a negative correlation with pan evaporation. Results showed a slightly better performance of the ANN models over the MLR models for the prediction of monthly pan evaporation in the study area.

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Spatiotemporal Variability of Potential Evaporation in Heihe River Basin Influenced by Irrigation

10.5194/egusphere-egu2020-12520 ◽

2020 ◽

Author(s):

Congying Han

Keyword(s):

Wind Speed ◽

Relative Humidity ◽

Spatial Pattern ◽

River Basin ◽

Meteorological Factors ◽

Heihe River Basin ◽

Spatiotemporal Variability ◽

Maximum Temperature ◽

Heihe River ◽

Potential Evaporation

Spatiotemporal Variability of Potential Evaporation in Heihe River Basin Influenced by Irrigation Congying Han1,2, Baozhong Zhang1,2, Songjun Han1,21 State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China.2 National Center of Efficient Irrigation Engineering and Technology Research-Beijing, Beijing 100048, China.Corresponding author: Baozhong Zhang ([email protected])Abstract: Potential evaporation is a key factor in crop water requirement estimation and agricultural water resource planning. The spatial pattern and temporal changes of potential evaporation calculated by Penman equation (EPen) (1970-2017) in Heihe River Basin (HRB), Northwest China were evaluated by using data from 10 meteorological stations, with a serious consideration of the influences of irrigation development. Results indicated that the spatial pattern of annual EPen in HRB was significantly different, among which the EPen of agricultural sites (average between 1154 mm and 1333 mm) was significantly higher than that of natural sites (average between 794 mm and 899 mm). Besides, the coefficient of spatial variation of the aerodynamic term (Eaero) was 0.4, while that of the radiation term (Erad) was 0.09. The agricultural irrigation water withdrawal increased annually before 2000, but decreased significantly after 2000 which was influenced by the agricultural development and the water policy. Coincidentally, the annual variation of Epen in agricultural sites decreased at -40 mm/decade in 1970-2000 but increased at 60 mm/decade in 2001-2017, while that in natural sites with little influence of irrigation, only decreased at -0.5mm/decade in 1970-2000 but increased at 11 mm/decade in 2001-2017. So it was obvious that irrigation influenced Epen significantly and the change of Epen was mainly caused by the aerodynamic term. The analysis of the main meteorological factors that affect Epen showed that wind speed had the greatest impact on Epen of agricultural sites, followed by relative humidity and average temperature, while the meteorological factors that had the greatest impact on Epen of natural sites were maximum temperature, followed by wind speed and relative humidity.

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Importance of solar radiation, temperature, relative humidity, and wind speed for calculation of reference evapotranspiration

Archives of Agronomy and Soil Science ◽

10.1080/03650340.2014.925107 ◽

2014 ◽

Vol 61 (2) ◽

pp. 239-255 ◽

Cited By ~ 52

Author(s):

Mohammad Valipour

Keyword(s):

Wind Speed ◽

Relative Humidity ◽

Solar Radiation ◽

Reference Evapotranspiration ◽

Radiation Temperature

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Using ERA-Interim Reanalysis output for creating datasets of energy-relevant climate variables

10.5194/essd-2016-67 ◽

2017 ◽

Cited By ~ 1

Author(s):

Philip D. Jones ◽

Colin Harpham ◽

Alberto Troccoli ◽

Benoit Gschwind ◽

Thierry Ranchin ◽

...

Keyword(s):

Climate Change ◽

Wind Speed ◽

Relative Humidity ◽

Solar Radiation ◽

Climate Variables ◽

Climate Data ◽

Near Surface ◽

Bias Adjustment ◽

Dewpoint Temperature ◽

Data Store

Abstract. The construction of a bias-adjusted dataset of climate variables at the near surface using ERA-Interim Reanalysis is presented. A number of different bias-adjustment approaches have been proposed. Here we modify the parameters of different distributions (depending on the variable), adjusting those calculated from ERA-Interim to those based on gridded station or direct station observations. The variables are air temperature, dewpoint temperature, precipitation (daily only), solar radiation, wind speed and relative humidity, available at either 3 or 6 h timescales over the period 1979-2014. This dataset is available to anyone through the Climate Data Store (CDS) of the Copernicus Climate Change Data Store (C3S), and can be accessed at present from (ftp://ecem.climate.copernicus.eu). The benefit of performing bias-adjustment is demonstrated by comparing initial and bias-adjusted ERA-Interim data against observations.

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Future climate uncertainty and spatial variability over Tamilnadu State, India

Global NEST Journal ◽

10.30955/gnj.001516 ◽

2015 ◽

Vol 17 (1) ◽

pp. 175-185

Keyword(s):

Wind Speed ◽

Relative Humidity ◽

Solar Radiation ◽

Minimum Temperature ◽

Climate Models ◽

Regional Climate ◽

Future Climate ◽

Maximum Temperature ◽

Projection Data ◽

Climate Uncertainty

<div> The present study analyses future climate uncertainty for the 21st century over Tamilnadu state for six weather parameters: solar radiation, maximum temperature, minimum temperature, relative humidity, wind speed and rainfall. The climate projection data was dynamically downscaled using high resolution regional climate models, PRECIS and RegCM4 at 0.22°x0.22° resolution. PRECIS RCM was driven by HadCM3Q ensembles (HQ0, HQ1, HQ3, HQ16) lateral boundary conditions (LBCs) and RegCM4 driven by ECHAM5 LBCs for 130 years (1971-2100). The deviations in weather variables between 2091-2100 decade and the base years (1971-2000) were calculated for all grids of Tamilnadu for ascertaining the uncertainty. These deviations indicated that all model members projected no appreciable difference in relative humidity, wind speed and solar radiation. The temperature (maximum and minimum) however showed a definite increasing trend with 1.8 to 4.0°C and 2.0 to 4.8°C, respectively. The model members for rainfall exhibited a high uncertainty as they projected high negative and positive deviations (-379 to 854 mm). The spatial representation of maximum and minimum temperature indicated a definite rhythm of increment from coastal area to inland. However, variability in projected rainfall was noticed. </div>

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Using ERA-Interim reanalysis for creating datasets of energy-relevant climate variables

Earth System Science Data ◽

10.5194/essd-9-471-2017 ◽

2017 ◽

Vol 9 (2) ◽

pp. 471-495 ◽

Cited By ~ 19

Author(s):

Philip D. Jones ◽

Colin Harpham ◽

Alberto Troccoli ◽

Benoit Gschwind ◽

Thierry Ranchin ◽

...

Keyword(s):

Climate Change ◽

Wind Speed ◽

Relative Humidity ◽

Solar Radiation ◽

Climate Variables ◽

Climate Data ◽

Near Surface ◽

Bias Adjustment ◽

Dewpoint Temperature ◽

Data Store

Abstract. The construction of a bias-adjusted dataset of climate variables at the near surface using ERA-Interim reanalysis is presented. A number of different, variable-dependent, bias-adjustment approaches have been proposed. Here we modify the parameters of different distributions (depending on the variable), adjusting ERA-Interim based on gridded station or direct station observations. The variables are air temperature, dewpoint temperature, precipitation (daily only), solar radiation, wind speed, and relative humidity. These are available on either 3 or 6 h timescales over the period 1979–2016. The resulting bias-adjusted dataset is available through the Climate Data Store (CDS) of the Copernicus Climate Change Data Store (C3S) and can be accessed at present from ftp://ecem.climate.copernicus.eu. The benefit of performing bias adjustment is demonstrated by comparing initial and bias-adjusted ERA-Interim data against gridded observational fields.

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A Study on Characteristics and Comparison of Evaporation Estimation Methods in Bandung

Journal of Mathematical and Fundamental Sciences ◽

10.5614/j.math.fund.sci.2021.53.2.2 ◽

2021 ◽

Vol 53 (2) ◽

pp. 182-199

Author(s):

Rusmawan Suwarman ◽

Novitasari Novitasari ◽

I Dewa Gede Agung Junnaedhi

Keyword(s):

Regression Analysis ◽

Wind Speed ◽

Relative Humidity ◽

Solar Radiation ◽

Vapour Pressure ◽

Vapour Pressure Deficit ◽

Estimation Methods ◽

Observation Data ◽

Weather Factor ◽

Better Than

This study aims to understand the characteristic of evaporation and to evaluate the evaporation estimation methods to be employed in Bandung by using observation data at three different land cover characteristics sites, namely, densely vegetated area (Baleendah), densely built-up area (Ujung Berung), and mix of buildings and vegetation area (ITB). Observation data used are hourly evaporation, vapour pressure deficit, temperature, relative humidity, wind speed, and radiation. The analysis was done mostly by using statistical methods such as regression analysis and error comparison. The result shows the dominant weather factor affecting the evaporation in ITB and Ujung Berung is vapour pressure deficit, and in Baleendah is solar radiation. The methods of evaporation estimations used in this study are Trabert, Schendel, Turc, and CIMIS-Penman methods. The result shows that the original constant values of those methods are significantly correlated. However, the Schendel is found the most overestimated, and the second is Turc. The best estimated evaporation in Baleendah, ITB, and Ujung Berung is calculated using CIMIS-Penman with one hour lag of radiation, Trabert, and Calibrated Schendel, respectively. The improvement of constant value was applied to Schendel and the result is better than the original constants.

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Time Series and Empirical Orthogonal Transformation Using Meteorological Parameters across the Climatic Zones in Nigeria

International Journal of Environment and Climate Change ◽

10.9734/ijecc/2019/v9i530113 ◽

2019 ◽

pp. 257-272

Author(s):

D. O. Akpootu ◽

B. I. Tijjani ◽

U. M. Gana

Keyword(s):

Time Series ◽

Relative Humidity ◽

Solar Radiation ◽

Meteorological Parameters ◽

Orthogonal Transformation ◽

Global Solar Radiation ◽

Climatic Zones ◽

Sunshine Hours ◽

Dry Seasons ◽

The Mean

Time series and empirical orthogonal transformation analysis was carried out for four (4) selected tropical sites, which are situated across the four different climatic zones, viz. Sahelian, Midland, Guinea savannah and Coastal region in Nigeria using measured monthly average daily global solar radiation, maximum and minimum temperatures, sunshine hours, rainfall, wind speed, cloud cover and relative humidity meteorological data during the period of thirty one years (1980-2010). Seasonal Auto Regressive Integrated Moving Average (ARIMA) models were developed along with their respective statistical indicators of coefficient of determination (R2), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE). The results indicated that the models were found suitable for one step ahead global solar radiation forecast for the studied locations. Furthermore, the results of the time series analysis revealed that the model type for all the meteorological parameters show a combination of simple seasonal with one or more of either ARIMA, winter’s additive and winter’s multiplicative with the level been more significant as compared to the trend and seasonal variations for the exponential smoothing model parameters in all the locations. The results of the correlation matrix revealed that the global solar radiation is more correlated to the mean temperature except for Akure where it is more correlated to the sunshine hours; the mean temperature is more correlated to the global solar radiation; the rainfall is more correlated to the relative humidity and the relative humidity is more correlated to the rainfall in all the locations. The results of the component matrix revealed that three seasons are identified in Nguru located in the Sahelian region namely, the rainy, the cool dry (harmattan) and the hot dry seasons while in Zaria, Makurdi and Akure located in the Midland, Guinea savannah and Coastal zones two distinct seasons are identified namely, the rainy and dry seasons.

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Long-term weather, hydrometric, and water chemistry datasets in high-temporal resolution at the La Salle River watershed in Manitoba, Canada

10.5194/essd-2017-57 ◽

2017 ◽

Cited By ~ 2

Author(s):

Marcos R. C. Cordeiro ◽

Jason A. Vanrobaeys ◽

Henry F. Wilson

Keyword(s):

Wind Speed ◽

Relative Humidity ◽

Solar Radiation ◽

Water Chemistry ◽

Temporal Resolution ◽

Time Step ◽

Canadian Prairies ◽

River Watershed ◽

Stream Discharge

Abstract. Lack of long-term datasets in fine temporal resolution hinders environmental studies and modelling efforts; to address this issue in the La Salle River watershed, in Canada, long-term weather (1990–2013), hydrometric (1990–2013 except years with no or poor data), and water chemistry (2009–2013) datasets were developed. The weather variables consisted of temperature, relative humidity, wind speed, solar radiation, and precipitation in an hourly time-step, which is required for physically-based modelling. The only hydrometric variable included in the dataset was stream discharge in a daily time-step, which is the usual time-frame for summarizing the results of long-term studies. The water chemistry data consisted of total nitrogen (TN), total dissolved nitrogen (TDN), total phosphorus (TP) and total dissolved phosphorus (TDP). Samples were collected weekly during the open water season at the same site as they hydrometric gauging station (05OG008) starting in August 2009 until October 2012 with some gaps (i.e. Fall 2011, Spring 2012, September 2012). In 2013 the frequency of sampling was increased to daily or sub-daily during high stream discharge and weekly during low stream discharge. An overview of the data indicates that values and trends are within ranges reported in the literature for the region. Mean annual, winter, and summer temperatures were 3.5 °C–10.7 °C and 17.2 °C, respectively. Annual relative humidity averaged 73.1 % but tended to be higher and more homogenous in cold seasons. Wind speed was very similar over the different seasons with annual average of 4.3 m/s. Solar radiation followed the typical curve reported for western Canada, with peak daily average values around 250 W/m2 in July. The precipitation records were mostly comprised of dry hours and the characteristic precipitation pattern of the Canadian Prairies with high frequency of small precipitation events as observed, with 75.3 % of the hourly precipitation being equal or less than 2 mm/h. The hydrometric characteristics of the dataset were also typical of the Canadian Prairies; the average peak discharge over the entire period was larger in April (2.3 m3/s) due to large amounts of snowmelt runoff. The average concentrations of TN, TDN, TP and TDP of 1.54, 1.35, 0.56, and 0.49 mg/L, respectively, were in agreement with values found in previous studies at the same location. The datasets for weather (https://doi.org/10.23684/ODI-2017-00957), discharge (https://doi.org/10.23684/ODI-2017-00959) and water chemistry (https://doi.org/10.23684/ODI-2017-00958) are accessible through the Government of Canada's Open Data portal (http://open.canada.ca).

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Investigation on the Temperature Distribution of Asphalt Overlay on the Existing Cement Concrete Pavement in Hot-Humid Climate in Southern China

Advances in Civil Engineering ◽

10.1155/2021/2984650 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Zuzhong Li ◽

Yayun Zhang ◽

Chunguang Fa ◽

Xiaoming Zou ◽

Haiwei Xie ◽

...

Keyword(s):

Wind Speed ◽

Relative Humidity ◽

Solar Radiation ◽

Prediction Model ◽

Air Temperature ◽

Concrete Pavement ◽

Humid Climate ◽

Temperature Prediction ◽

Asphalt Overlay ◽

Hot Humid Climate

Temperature is known to be one of the most important factors affecting the design and performance of asphalt concrete pavement. The distresses of asphalt overlay are closely related to its temperature, particularly in Guangxi, a hot-humid-climate region in China. This research is to analyze the impact of meteorological factors on temperature at 2 cm depth in asphalt overlay by ReliefF algorithm and also obtain the temperature prediction model using MATLAB. Two test sites were installed to monitor the temperatures at different pavement depths from 2014 to 2016; meanwhile, the meteorological data (including air temperature, solar radiation, wind speed, and relative humidity) were collected from the two meteorological stations. It has been found that the temperature at 2 cm depth experiences greater temperature variation, and the maximum and minimum temperatures of asphalt overlay, respectively, occur at 2 cm depth and on the surface. Besides, the results of ReliefF algorithm have also shown that the temperature at 2 cm depth is affected significantly by solar radiation, air temperature, wind speed, and the relative humidity. Based on these analyses, the prediction model of maximum temperature at 2 cm depth is developed using statistical regression. Moreover, the data collected in 2017 are used to validate the accuracy of the model. Compared with the existing models, the developed model was confirmed to be more effective for temperature prediction in hot-humid region. In addition, the analysis of rutting depth and overlay deformation for the two test sections with different materials is done, and the results have shown that reasonable structure and materials of asphalt overlay are vital to promote the high-temperature antideforming capability of pavement.

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